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The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria.

Sha Joe ZhuJason A HendryJacob Almagro-GarciaRichard D PearsonRoberto AmatoAlistair MilesDaniel J WeissTim Cd LucasMichele NguyenPeter W GethingDominic KwiatkowskiGilean McVeannull null
Published in: eLife (2019)
Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Furthermore, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.
Keyphrases
  • plasmodium falciparum
  • escherichia coli
  • risk factors
  • electronic health record
  • big data
  • single cell
  • dna methylation
  • quality improvement
  • genome wide
  • artificial intelligence